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Abstract Wireless energy transfer (WET) based on ultrasound‐driven generators with enormous beneficial functions, is technologically in progress by the valuation of ultrasonic metamaterials (UMMs) in science and engineering domains. Indeed, novel metamaterial structures can develop the efficiency of mechanical and physical features of ultrasound energy receivers (US‐ETs), including ultrasound‐driven piezoelectric and triboelectric nanogenerators (US‐PENGs and US‐TENGs) for advantageous applications. This review article first summarizes the fundamentals, classification, and design engineering of UMMs after introducing ultrasound energy for WET technology. In addition to addressing using UMMs, the topical progress of innovative UMMs in US‐ETs is conceptually presented. Moreover, the advanced approaches of metamaterials are reported in the categorized applications of US‐PENGs and US‐TENGs. Finally, some current perspectives and encounters of UMMs in US‐ETs are offered. With this objective in mind, this review explores the potential revolution of reliable integrated energy transfer systems through the transformation of metamaterials into ultrasound‐driven active mediums for generators.

Background & aim: Opportunistic fungal infections following COVID-19 is problematic. Antifungal medications are as well used to treat people who have fungal infections. The aim of the present study was to evaluate the prevalence of fungal infections and the usage of antifungal medications before and after the COVID-19 pandemic. Methods: The present descriptive, analytical, and retrospective study was conducted in 2021.The records of patients who were hospitalized in Abadan hospitals in the year before or the first two years of the COVID-19 pandemic and received at least one of the antifungal medication’s fluconazole, caspofungin, or amphotericin B were extracted and assessed for the present study. Demographic information of patients, type of ward, and type of fungal infection, history of previous infection with covid-19 and severity of disease were extracted from patient files. Descriptive analysis, chi-square, and ANOVA statistics were used to analysis the data. Results: The results indicated that the mucormycosis and aspergillus fungal infections had dramatically increased in the two years since the COVID-19 pandemic initiated. Although it was not statistically significant, the usage of antifungal medications rose in the two years following the beginning of COVID-19 compared to the prior two years. The mucormycosis fungus was the most prevalent fungal infection. The lung was the most commonly infected organ. All patients with fungal infection had a history of severe COVID-19 following the COVID-19 pandemic. The most often used medication was amphotericin B. This difference was substantial (P< 0.001) when aspergillosis patients received capsofungin, mucormycosis patients received amphotericin B, and candida patients received fluconazole. Conclusion: The present indicated that aspergillosis and mucormycosis fungal infections were more common during the COVID-19 pandemic, and as a result, antifungal medication use has increased as well. Additionally, all three medications (fluconazole, caspofungin, and amphotericin B) were administrated according guidelines.

In this paper, optimization of a system of square cascades for separation of middle isotopes is introduced, and the optimal square cascade for use in this system is presented. In this case, 130Xe which is a middle isotope, is presented as an example. The separation of this isotope is much more complex than that of other isotopes. The Grey Wolf Algorithm is applied for the optimization, and the parameters of cascade feed flow rate, cut of the cascade, feed location, feed flow of gas centrifuges (GC), and the cut of the first stage are optimized in such a way that the maximum recovery of the target isotope and the maximum cascade capacity are obtained. The optimization results showed that for the nine selected cascades with 180 GCs, the more stages the cascade has, the less number of separation steps are required and the recovery factor and the amount of product increase.

IntroductionSugarcane is a strategic agricultural product and increasing productivity and self-sufficiency in its production is of special importance. The most important product of sugarcane is sugar. Various factors like climatic and management conditions affect the yield of sugarcane and recoverable sugar. Crop yield forecasting is one of the most important topics in precision agriculture, which is used to estimate yield, match product supply with demand and manage product to increase productivity. The purpose of this study is to predict and model the factors affecting sugar extracted from sugarcane (recoverable sugar) in the farms of Amir-Kabir sugarcane agro-industry Company of Khuzestan province using machine learning methods.Materials and MethodsTo conduct this study, data from the agro-industrial company Amir-Kabir in the province of Khuzestan from 2010 to 2017 were used. This data has 3223 records which include four sets of data: climate, soil, crop and farm management. This data includes continuous and discrete variables. Discrete variables include production management, soil type, farm, variety, age (cane class), the month of harvest and times irrigation. Continuous variables include area, chemical fertilizer consumption, water consumption per hectare, total water consumption, drain, crop season duration, yield (cane yield) soil EC, purity, time interval drying off to crop harvest, precipitation, min and max temperature, min and max relative humidity, wind speed and evaporation. The recoverable sugar variable is considered as the target variable and is divided into two classes, values greater than or equal to 9 are in the optimal class and less than 9 are in the undesirable class. The other variables are considered as predictor variables. For modeling using the Holdout method the data were randomly divided into two independent sets, a training set and a test set. 70% of the data which includes 2256 records were used for training and 30% of the data which includes 967 records were used for testing. The modeling of this study was performed with the Python programming language version 3.8.6 in the Jupyter notebook environment. Random Forest, Adaboost, XGBoost and SVM (support vector machine) algorithms were used for modeling.Results and DiscussionTo evaluate the models, metrics of accuracy, precision, recall, f1 score and k-fold cross validation were used. The XGBoost model with 94.8% accuracy on the training set and the Adaboost model with 92.4% accuracy on the test set, are the best models. Based on precision and recall metrics Adaboost model with 87% precision and SVM model with 87% recall have better performance than the other models. Based on Repeated 10-fold stratified cross validation using two repeats the SVM model with 92.3% accuracy is the best model. The variables of purity, time interval drying off to crop harvest and crop season duration are the most important variables in predicting the recoverable sugar.ConclusionIn this study a new approach based on machine learning methods for predicting recoverable sugar from sugarcane was presented. The most important innovation of this study is the simultaneous consideration of management and climatic factors, along with other factors such as soil and crop characteristics for modeling and classification the recoverable sugar percentage from sugarcane. The results show that the performance of all models is acceptable and machine learning methods and ensemble learning algorithms can be used to predict crop yield. The results of this study and the analysis of the rules obtained from the set of decision trees made in the random forest model can be used for managers of different agro-industries in determining appropriate strategies and preparing the conditions to achieve optimal production.For future research as well as policy making and decision making Amir-Kabir sugarcane agro-industry Company the following suggestions are offered: more samples can be used to obtain more reliable results. Also can be used Deep learning methods, time series analysis and image processing. Use of IOT equipment to collect and real-time processing data on Amir-Kabir sugarcane agro-industry farms.

Abstract There is a need for point‐of‐care bacterial sensing and identification technologies that are rapid and simple to operate. Technologies that do not rely on growth cultures, nucleic acid amplification, step‐wise reagent addition, and complex sample processing are the key for meeting this need. Herein, multiple materials technologies are integrated for overcoming the obstacles in creating rapid and one‐pot bacterial sensing platforms. Liquid‐infused nanoelectrodes are developed for reducing nonspecific binding on the transducer surface; bacterium‐specific RNA‐cleaving DNAzymes are used for bacterial identification; and redox DNA barcodes embedded into DNAzymes are used for binding‐induced electrochemical signal transduction. The resultant single‐step and one‐pot assay demonstrates a limit‐of‐detection of 102 CFU mL−1, with high specificity in identifying Escherichia coli amongst other Gram positive and negative bacteria including Klebsiella pneumoniae, Staphylococcus aureus, and Bacillus subtilis. Additionally, this assay is evaluated for analyzing 31 clinically obtained urine samples, demonstrating a clinical sensitivity of 100% and specify of 100%. When challenging this assay with nine clinical blood cultures, E. coli‐positive and E. coli‐negative samples can be distinguished with a probability of p

Nowadays, vending is the most important pillar of a production set. The positive and significant points of the manufactured parts are attained by interviewing with the customer, sending a questionnaire to the customer, testing the market, investigating the quality and reliability of new products, probing the reports and product quality of competitors. Aimed at predicting the type of leadership in reverse engineering, based on “Voice of Customer” (VOC), between the Iran Khodro Company’s (IKC) managers (case study of Peugeot 405 brake pump shell), the present study was conducted. Using descriptive-correlation research method, about 90 managers were randomly selected in several categories according to gender, age and years of service, proportional to the size of each category. Three questionnaires of “Oregon and Kanowski Organizational Citizenship Behavior” (OKOCB), “Fry Spiritual Leadership” (FSL), and researcher-made “Charismatic Leadership” (CL) were utilized to collect the research data. To measure the reliability rate using Cronbach's alpha coefficient, this item was obtained for all three questionnaires of the OKOCB, FSL and CL behavior (α = 0.92, 0.81 and 0.85) respectively. Collecting the data, they were analyzed using multiple regression analysis and based on the research hypotheses. The results indicated that there is a positive and significant relationship between the dimensions of SL and organizational citizenship behavior, and also between the CL and the organizational citizenship behavior. The results of structural equation modeling also showed that SL with a path coefficient 0.7 is able to affect overall quality management (t ≥ 1.96, p≤ 0.05).

Abstract To prevent surgical site infection (SSI), which significantly increases the rate morbidity and mortality, eliminating microorganisms is prominent. Antimicrobial resistance is identified as a global health challenge. This work proposes a new strategy to eliminate microorganisms of deep tissue through electrical stimulation with an ultrasound (US)‐driven implantable, biodegradable, and vibrant triboelectric nanogenerator (IBV‐TENG). After a programmed lifetime, the IBV‐TENG can be eliminated by provoking the on‐demand device dissolution by controlling US intensity with no surgical removal of the device from the body. A voltage of ≈4 V and current of ≈22 µA generated from IBV‐TENG under ultrasound in vitro, confirming inactivating ≈100% of Staphylococcus aureus and ≈99% of Escherichia coli. Furthermore, ex vivo results show that IBV‐TENG implanted under porcine tissue successfully inactivates bacteria. This antibacterial technology is expected to be a countermeasure strategy against SSIs, increasing life expectancy and healthcare quality by preventing microorganisms of deep tissue.

In designing the fuel rod, having a reliable prediction of fuel performance is of high importance in order to comply with safety principles. Various computer codes have been provided for this purpose. Each of these codes uses different mechanical models, numerical and analytical methods. Accordingly, the purpose of the present work is to develop Fortran computer software for mechanical and thermal analysis of fuel rods using numerical methods, especially applying the principle of virtual work in mechanical analysis of fuel rods in steady-state conditions. The finite element method is used to solve the equations. The mechanical analysis includes phenomena such as swelling and fuel density and clad creep. Through these phenomena and simultaneous performance of mechanical and thermal analysis, fuel-clad interaction, stress and strain rate in fuel and clad, fuel center temperature, oxide layer thickness, fuel, and clad temperature distribution during operation is obtained by the code. The results of the code are compared with the results of the analytical method which are available in other research works. Finally, for the VVER1000 reactor, mechanical and thermal analysis of the fuel rod was performed over a 1600-day interval. According to the simulation, the fuel-clad interaction occurs after 1250 days.

Background & aim: Migraine is a major, common and debilitating headache disorder that affects 10 to 20 percent of the population, especially the working age, and is one of the 20 most debilitating diseases in the world. The aim of this study was to determine and compare the effectiveness of the treatment group based on acceptance and commitment and cognitive behavioral therapy on the experimental and cognitive avoidance of patients with migraine headaches. Methods: This study is a semi experimental study that was done in 2016. The statistical population of the study consisted of 30 patients with migraine headaches who were selected through targeted sampling and after randomized entry and exit of the research conditions in three groups; treatment based on acceptance and commitment, cognitive behavioral therapy and allocation control. Were given. In order to collect information, Ahvaz Najarianian Migraine Questionnaires, Admission and Practice - Second Edition(AAQ-II) and Cognitive Misunderstanding of Guilders et al. were used. The scores obtained from the evaluations were analyzed in three stages (pre-test, post-test and follow-up) with the help of one-way analysis of variance(Anova), Levin and Toki follow-up. Result: The results indicated the effect of acceptance-based therapy and commitment on experimental avoidance and cognitive coherence at two levels: short-term(p

Background and Objectives: Target detection is one of the main applications of remote sensing. Multispectral (MS) images with higher spatial resolution than hyperspectral images are an important source for shape and geometric characterization, and so, MS target detection is interested.Methods: A target detector appropriate for multispectral (MS) images is selected among hyperspectral target detectors and redefined in this paper. Many target detectors have been proposed for hyperspectral images in the remote sensing filed. Most of these detectors just use the spectral information. Since, the MS images have higher spatial resolution compared to hyperspectral ones, it is proposed that select a target detector that uses both of the spectral and spatial features. To this end, the attribute profile based collaborative representation (AP-CR) hyperspectral detector is chosen for MS images. Shape structures extracted by flexible attribute filters can significantly improve the MS target detection.Results: As a case study, the wheat fields in Chenaran County in Iran are chosen as targets to be detected. The image acquired by Landsat 8 is used for doing experiment. The results show the superior performance of AP-CR with 96.09 % accuracy for wheat detection using MS image of Landsat 8.Conclusion: The high performance of AP-CR is due to extraction of flexible attribute characteristics and the use of collaborative representation for approximation of each image pixel. Although the AP-CR method provides the highest accuracy, it needs a high running time compared to other detectors.

This study was conducted to analyse the serine protease of Fasciola hepatica eggs by specific sub-strates and inhibitors, and investigation of the effects of pH and temperature on proteases’ activity and stability. Adult worms were isolated from infected livers. After homogenisation, their protein levels were measured with the Bradford method. Total proteolytic activity of the Fasciola hepatica extract was evaluated with azocasein substrate at pH values from 2 to 12. N-benzoyl–arginine–p-nitroanilide (BApNA) trypsin and N-succinyl-alanine-alanine-prolin-phenylalanine-p-nitroanilide (SAAPFpNA) chymotrypsin substrates were used to measure specific protease activities. The effect of protease inhibitors phenylmethane sulfonyl fluoride (PMSF), pepsin, and ethylenediaminetetraacetic acid (EDTA) on these enzymes was evaluated. Estimation of optimum temperature and pH was per-formed in the temperature range of 10–90 °C and pH values from 2–12. The optimum pH activities for trypsin and chymotrypsin were at alkaline pH and for total proteolytic activity at acidic pH. The results using protease inhibitors showed that the eggs had serine protease activity. The optimum tem-perature activity of trypsin and chymotrypsin was 50 °C. These proteases were stable up to 40 °C. Due to the importance of pH and temperature in life cycle of Fasciola hepatica, these findings can be used for induction of some modifications in pH and preventing the activity of the enzyme for decre-ment of the efficacy of embryonic development and egg hatching of this zoonotic parasite.

Electricity demand forecasting is an important task in power grids. Most of researches on electrical load forecasting have been done in the time domain. But, the electrical time series has a non-stationary inherence that makes hard load prediction. Moreover, valuable information is hidden in the electrical load sequence which is not open in the time domain. To deal with these difficulties, a new electricity demand forecasting framework is proposed in this work. In the proposed framework, at first, a new feature space of electrical load sequence is composed. The provided domain involves complementary information about shape and variations of electrical load sequence. Then, the obtained load features are integrated with the original load values in time domain to allow a rich input for predictor. Finally, a powerful deep learning technique from the family of recurrent neural networks, named long-short term memory, is used to learn electricity demand from the provided features in single and hybrid domains. The following domains are investigated in this work: frequency, cepstrum, spectral centroid, spectral roll-off, spectral flux, energy, time difference, frequency difference, Gabor and collaborative representation. The experiments show that the use of time difference domain decreases the mean absolute percent error from 0.0332 to 0.0056.

Due to abundant spectral information contained in the hyperspectral images, they are suitable data for anomalous targets detection. The use of spatial features in addition to spectral ones can improve the anomaly detection performance. An anomaly detector, called nonparametric spectral-spatial detector (NSSD), is proposed in this work which utilizes the benefits of spatial features and local structures extracted by the morphological filters. The obtained spectral-spatial hypercube has high dimensionality. So, accurate estimates of the background statistics in small local windows may not be obtained. Applying conventional detectors such as Local Reed Xiaoli (RX) to the high dimensional data is not possible. To deal with this difficulty, a nonparametric distance, without any need to estimate the data statistics, is used instead of the Mahalanobis distance. According to the experimental results, the detection accuracy improvement of the proposed NSSD method compared to Global RX, Local RX, weighted RX, linear filtering based RX (LF-RX), background joint sparse representation detection (BJSRD), Kernel RX, subspace RX (SSRX) and RX and uniform target detector (RX-UTD) in average is 47.68%, 27.86%, 13.23%, 29.26%, 3.33%, 17.07%, 15.88%, and 44.25%, respectively.

Remote sensing image classification is a method for labeling pixels to show the Land cover types. The ambiguity in the classification process can be reduced if the spatial dependencies, which exist among the adjacent pixels, are intelligently incorporated into the feature extraction process. One of the methods for spatial feature extraction is applying morphological filters. The basic idea of the morphological filters is comparison of structures within the image with a reference form called structural element. Four types of important morphological filters are included (dilation, erosion, opening, and closing) in this work. Opening morphological filter is used to extract spatial features where this filter is implemented by applying two successive sequences dilation and erosion operators. This filter removes the light areas smaller than the structural element in binary images; and in the gray level images, the areas smaller than the structural element and brighter than the neighboring regions are removed. Differential morphology filters are other important morphological filters, which are also used in this work. In the proposed method, the principal component analysis is used to reduce the data dimensions and an SVM classifier is applied to classify the hyperspectral data. The proposed method provides better classification results than the conventional morphological profile about 2%-5% for the University of Pavia and Pavia Center datasets. The results represent the good performance of the proposed method by using a small number of training samples.

One of the challenges of the hyperspectral image classification is the fusing spectral and spatial features. There are several methods for fusing features in hyperspectral image classification. Three-Dimensional Gabor Filters are the best method to extract spectral and spatial features simultaneously. However, one of the problems with using the 3D Gabor filter is the high number of extracted features. In this paper, to reducing extracted features from 3D-Gabor filters and increasing the classification accuracy in hyperspectral images, a novel method named Local Binary Graph (LBG) is used. The LBG method uses a local graph to solve the optimization problem, which maps each pixel to the reduced dimension image and improves the McNemar test result in comparison with the existing methods. Finally, the result of the proposed method achieved 96.2% and 92.6% overall accuracy for Pavia University and Indian Pines data set, respectively.

Aerosols are tiny liquid or solid particles suspended in the air have a strong influence on air quality, human health and climate change. Sun-photometer is a tool for measuring the optical and physical characteristics of aerosols by using sunlight at various wavelengths from the Earth surface. In this paper, the design, construction, and calibration of a single channel sun-photometer is presented. Aerosols optical depth is amount of suspended particles in the Earth atmosphere was measured by the sun-photometer on September 5, 2016, and the results are in good agreement with the measurements of a CE 318-2 sun-photometer manufactured by the French CIMEL company.

Background & Aim: According to the disturbance in testis circulation and subsequently the anemia of an organ, it can disrupt fertility. In this study, the protective effect of mesenchymal gastric lavage cells on testicular histomorphometry after the torsion of Tursion-Detursion (T-D) of the testicular band was investigated in a comparative manner. Methods: In this experimental study, 30 adult mice were randomly divided into three equal groups; the control group was Sham, in this group only the abdominal cavity was opened and then closed. Torsion Torsion Group (T-D), in this group, the fracture torsion was performed in the left testicular fracture. After the circulating blood was restored to the rat's ellipse, a phosphate buffer of 10 μL was injected. Mesenchymal cell group (T-D + Msc), after torsion of the defect in mice, the mesenchymal cells were injected into half a million volumes, 10 microliters, of the testicular network. At the end of the treatment period (35 days), after sampling the testicular tissue of the left side and stabilizing the sample, paraffin sections were stained with Hematoxylin Eosin and Masonium trichrome (for cervical counts), then the tissue parameters were evaluated. The data were analyzed using ANOVA test and Tukey post hoc test. Results: Our results showed that mesenchymal treatment (T-D + MSc) had a significant increase in the mean spermatogenesis, active serotylated cells and leydig cells, diameter of the menisis tubes, epithelium thickness and testis capsule compared with the defect torsion group (TD) P

In this work, we report the synthesis of silver decamolybdate, Ag6Mo10O33, nanostructure by a simple mechanohemical process followed by calcination treatment using acetamide as driving agent. Morphological study by scanning electron microscopy (SEM) images revealed bundles of rods grown closely together with an average diameter of 92 nm for Ag6Mo10O33 sample. Ni-substituted Ag6Mo10O33 compound was prepared via introducing nickel cation to precursor system in mechanical milling step. The particle size decreased to 87 nm by incorporating nickel units in substituted polymolybdate. It was concluded that the suitable selection of reagents can direct solid phase reaction towards producing nanostructured products. This technique is easy and simple for preparation of various mixed metal oxides without using any solvents and or complex procedures. In addition, the photocatalytic activity of the prepared products was studied on the removal of 4-nitro phenol (4-NP) as organic pollutant from water. The obtained results were also discussed in detail.

Failure Modes and Effects Analysis (FMEA) is being widely used to detect and eliminate known and/or potential failures, problems, errors and so on from system design, process, and/or service, before they reach the customer. It can be done by calculating the risk priority number which is the product of three factors: occurrence, severity and detectability. A lot of efforts have been made to overcome the shortcomings of the crisp RPN calculation and extend it to fuzzy environment. In this study, the presented fuzzy approach allows experts to describe the variables of risk priority number using linguistic terms by applying the method of fuzzy axiomatic design (FAD). At the final part of this paper a hypothetical case study demonstrated the applicability of the FMEA model under fuzzy environment.

Feature extraction is a very important preprocessing step for classification of hyperspectral images. The linear discriminant analysis (LDA) method fails to work in small sample size situations. Moreover, LDA has poor efficiency for non-Gaussian data. LDA is optimized by a global criterion. Thus, it is not sufficiently flexible to cope with the multi-modal distributed data. We propose a new feature extraction method in this paper, which uses the boundary semi-labeled samples for solving small sample size problem. The proposed method, which called hybrid feature extraction based on boundary semi-labeled samples (HFE-BSL), uses a hybrid criterion that integrates both the local and global criteria for feature extraction. Thus, it is robust and flexible. The experimental results with three real hyperspectral images show the good efficiency of HFE-BSL compared to some popular and state-of-the-art feature extraction methods.

BACKGROUND AND OBJECTIVE: Investigation of the relationship between joint sounds and radiographic findings could be helpful in selecting a suitable treatment for internal temporomandibular joint disorder (TMJD). This study aimed to evaluate degenerative changes, condyle position and joint effusion in patients with TMJD using magnetic resonance imaging (MRI) as a gold standard procedure. METHODS: This cross-sectional study was conducted on 34 patients diagnosed with TMJD via MRI. MRI images were obtained from the sagittal plane of the subjects using 5.0 Tesla Magnetic Resonance Scanner with open and closed mouth. Click sounds were divided into three categories based on the origin of the sound while opening the mouth: premature (31 mm). In addition, effusion volume, condyle-fossa relationship, swelling of the joints and degenerative changes were evaluated in the patients. FINDINGS: Regarding the condyle position in the fossa, 32 of the examined joints (47.1%) were in central position, 30 (44.1%) were in posterior position, and 6 joints (8.8%) were in the upper position. Moreover, 34 joints (70.8%) had clicks, and 14 joints (70%) had no clicks or symptoms of osteoarthritis. Following that, grade-zero and grade-one effusions accounted for the highest number of examined joints, and there was no significant relationship between effusion volume and type of clicking. In the study group with clicking joints, 10 cases (14.7%) had premature clicks, 20 (29.4%) had intermediate clicks, and 18 joints (26.5%) had delayed clicks. CONCLUSION: According to the results of this study, presence or absence of premature, intermediate and delayed clicks have no effects on condylar position in the fossa, effusion volume and occurrence of osteoarthritis in MRI.

Hyperspectral sensors provide a large number of spectral bands. This massive and complex data structure of hyperspectral images presents a challenge to traditional data processing techniques. Therefore, reducing the dimensionality of hyperspectral images without losing important information is a very important issue for the remote sensing community. We propose to use overlap-based feature weighting (OFW) for supervised feature extraction of hyperspectral data. In the OFW method, the feature vector of each pixel of hyperspectral image is divided to some segments. The weighted mean of adjacent spectral bands in each segment is calculated as an extracted feature. The less the overlap between classes is, the more the class discrimination ability will be. Therefore, the inverse of overlap between classes in each band (feature) is considered as a weight for that band. The superiority of OFW, in terms of classification accuracy and computation time, over other supervised feature extraction methods is established on three real hyperspectral images in the small sample size situation.

Introduction: A great amount of evaluating performance and offering suggestions and solution to solve the problem of the government agencies is possible through performance audit. Moreover, implementing this type of auditing has been obliged in the fifth five-year plan of development. Therefore, investigating the challenges beyond the obligation of performance audit in the government agencies of Iran has a great significance. Method: The present research is an applied descriptive survey in which the viewpoints of four groups including university professors, the auditors of the Audit Organization, the auditors of the Supreme Court of Audit, and the managers and auditors of the government agencies in Tehran and Alborz provinces have been collected by questionnaire. Hence, the data of the research have been analyzed by SPSS Software Version 18, Chi-squared Test, One-Way Analysis of Variance, and Friedman Test. Results: The Results of the research indicate that there is no agreement among the tested groups on the most important challenge, but regarding the sum of the scores based on their viewpoints, “not using accrual basis of accounting in the government agencies” has been considered as the most important challenge. In the case of the least important challenge, there is a relative agreement in such a way that most groups consider “the lack of mental and behavioral independency of the performance auditors” as the least important challenge in implementing the performance audit, and according to the sum of the scores, “the lack of mental and behavioral independency of the performance auditors” is the least important challenge from the viewpoint of the subjects. Conclusion: The most important challenges beyond the performance audit in the government agencies of Iran are not using the accrual basis, the inadequacy of rules and regulations in this field, the governmental managers' insistence on adhering to the traditional methods, inappropriate budgeting system, the conflicts of priorities and values between legislators and executives, the lack of public response-claiming culture, the shortage of expert force, the lack of appropriate criteria and standard for performance audit, and the non-existence of powerful and influential private and professional formations.

Supervised classification of hyperspectral images is a difficult task due to the unbalance between the high dimensionality of the data and the limited availability of labeled training samples. Recently support vector machine (SVM), has received considerable attention for classifying high dimensional data and is applied successfully for classification of hyperspectral images because it discriminates classes by a geometrical criterion not by statistical criteria. In this paper, we investigate sensitivity of SVM classifier respect to two factors. The first factor is the dimensional of data (the number of features) and the second factor is the number of training samples. We evaluate the effect of these factors on the performance of classification in the point of view both accuracy and reliability. Experiments are carried out on the three different common used hyperspectral datasets, Indian pines, Pavia University and Salinas.

One of the most important and challenging problems in supervised classification of high dimensional data is limited available training samples. Using the parametric classifiers is not appropriate in this condition. Thus a new simple nonparametric supervised classifier based boundary samples of each class is proposed in this paper that need no statistic parameter for classification. Accuracy and reliability of this classifier is compared whit other non-parametric classifiers such as Parallelepiped (box), K nearest neighbours (KNN), Artifical Neural network (ANN) and SVM and also a parametric classifier that use only first order statistic, Minimum Euclidean Distance (MED), for different four datasets, AVARIS data, Pavia University, Pavia center and Salinas data. The results of experiments show that proposed classifier in despite of simplicity has appropriate and reasonable efficiency.

This study aims to characterize and evaluate the applicability of Polyhedral OligomericSilsesquioxane (POSS)/ Poly (carbonate-urea) Urethane (PCU) nanocomposite films as a temporary skin substitute by means of FTIR, MTT assay, cell proliferation assay and SEM studies. FTIR spectra showed all the characteristic peaks of POSS/PCU nanocomposite. The indirect cytotoxicity of membranes was investigated by MTT assay. In MTT test, L929 mouse fibroblasts were exposed to the extract of the films for 24 h. MTT results showed no sign of cell cytotoxicity for the extracts at the extraction times up to14 days. Menwhile, it was found that POSS nanocages have a stimulating effect on L929s. In cell proliferation assay, L929s were cultured on the films for 3, 7 and 14 days. The cells showed a high rate of proliferation in direct contact with the biomaterial after 7 and 14 days. Morphology and density of the cells on the nancomposite surface was investigated through SEM observations. SEM micrographs showed that the cells adhered well on the surface after 3 days of culture. Moreover, after 7 days, cell density increased so substantially that a cell layer was formed on the membranes.

Introduction: Improvement of the quality of nursing education involves continuous assessment of the current condition in order to improve the quality. The students who received educational services are the best source to recognize the existing problems in the education. This study aims to describe the experiences and viewpoints of nursing students on quality of nursing education Method: In this qualitative study, 40 senior nursing students were selected according to purposeful sampling method. Then, eight focus group interviews were performed. All interviews were transcribed and analyzed according to content analysis method. Results: Using thematic analysis, a significant number of preliminary themes and three main themes each with few other sub-themes, were emerged. The main themes were “theoretical education”, “clinical education” and “the gap between theoretical and clinical education”. Conclusion: It seems that theoretical and clinical education needs more comprehensive educational approaches to integrate knowledge and practice. We suggest that clinical assessment perform by faculties of nursing schools in clinical settings.

Cadmium oxide rhombus-shaped nanostructure was synthesized using hydrothermal process followed by heating treatment. Clearly, X-ray diffraction pattern demonstrated the formation of CdO crystalline phase. Scanning electron microscopy (SEM) showed that the obtained rhombus-like structure is composed of nanoparticles with the average size of 29 nm. In addition, we evaluated adsorption of organic dye i.e. Congo red from water using the prepared CdO rhombus like microstructure. UV-visible absorption spectroscopy was used to record the adsorption behavior. It was found that the removal process is performed via electrostatic absorption mechanism. The maximum adsorption capacity of CdO rhombus structures (0.01 g) for Congo Red (CR) in the concentration range (5-50 mg L-1 ) studied, as calculated from the Langmuir isotherm model at 25 ˚C and neutral pH, was found to be 41.20 mg g−1 .

Cauliflower-like nanostructure of cadmium oxide was synthesized by utilizing mechanochemical reaction followed calcination procedure for the first time. The design of experiment (DOE) by Taguchi method was used to study influence of the chosen factors and to consider optimum conditions of the experiments. The temperature of calcining, the duration of milling, the duration of calcining and reactants molar ratio (M/L) are four chosen factors for DOE. The value of band gap energy was calculated through UV-visible absorption spectroscopy and Tauc relation. Data analysis was performed using Minitab and Qualitek-4 statistical softwares. The optimum conditions for preparing cauliflower-like structure were predicted and also, influence of each factor was determined. The temperature and duration of calcining with the percentage contribution of 68.23 and 17.72%, respectively are the most effective factors at this design.

Introduction: In this research, we have studied the attitudes of war handicapped to provided services by Foundation of Martyrs and Veterans Affairs (FMVA). Method & Materials: The statistical population includes all war handicapped above 25% handicap in Qom province. In present study, the statistical population is grouped into three groups based on their handicap percentage and requirement: 25% - 49%, 50% - 69% and 70% and more. Based on research objectives, a structured questionnaire was provided on the basis of FMVA services and opinions of experts and connoisseurs in which provided services were categorized into four service groups: livelihood and welfare services training and cultural services health and treatment services and housing services. In the meantime, the quality of provided services was studied by SERVQUAL model. Results: The results of statistical tests show that 50% - 60% and above 70% war handicapped believe that their service are more desirable than 25% - 49% war handicapped. Also, the findings show that all three groups believe the training and cultural services are not well which necessitates a pathology. The findings of Freedman’s test show that all three groups of war handicapped have categorized the provided services similarly. Discussion:Discussion The findings of SERVQUAL questionnaire indicate that all groups believe that the highest gap is seen in the rapidness of services which necessitate pathology by officials as well as identifying and executing remedy guidelines. For other factors, the quality of services is desirable which we can improve them by continuous training planning.

The purpose of this study was to compare the efficacy of the cognitive-behavior therapy (CBT), exposure and response prevention (ERP) and Fluoxetine in the obsessive-compulsive patients. The experimental design was selected and 59 patients with obsessive-compulsive disorder (OCD) diagnosis were randomly assigned to 4 groups: cognitive-behavioral therapy, exposure and response prevention, Fluoxetine and waiting-list. Patients in the four groups were seleted with respect to the variables such as age, sex, academic and marital status. For gathering data Moudsley Obsessive-Compulsive Inventory (MOCI), Beck Depression Inventory-II (BDI-II), Beck Anxiety Inventory (BAI) were used as instruments. The patients of the four groups completed these three questionnaires in 3 stages (pre test, post test, follow-up with 2 months interval). The data were analysed by multivariate analyses of Anova (MANOVA-1 way), and post hoc test (T tukey). The findings indicated the treatment effect of CBT on OCD severity was more significant than Fluoxetine in short-term and long-term (P

Bioedegradable nanoparticles are intensively investigated for their potential applications in drug delivery systems. Being a biocompatible and biodegradable polymer, chitosan holds great promise for use in this area. This investigation was concerned with determination and optimization of the effective parameters involved in the production of chitosan nanoparticles using ionic gelation method. Studied variables were concentration and pH of the chitosan solution, the ratio of chitosan to sodium tripolyphosphate therein and the molecular weight of chitosan. For this purpose, Taguchistatistical method was used for design of experiments in three levels. The size of chitosan nanoparticle was determined using laser light scattering. The experimental results showed that concentration of chitosan solution was the most important parameter and chitosan molecular weight the least effective parameter. The optimum conditions for preparation of nanoparticles were found to be 1 mg/mL chitosan solution with pH=5, chitosan to sodium tripolyphosphate ratio of 3 and chitosan molecular weight of 200,000 daltons. The average nanoparticle size at optimum conditions was found to be about 150 nm.

One of man's fundamental needs is communication. Nurses within their specialty sense this need in their relationship with their patients, even though in some cases with physical impediments e.g. the placing of bypass tubes, this relationship is endangered. This study is a quasi experimental research, whose main aim is to compare the effects of two types of communication methods on anxiety and satisfaction in patients after cardiac and bypass surgery having intubation, warded at chosen hospitals affiliated to the Tehran University of Medical Sciences. According to the specific criteria, 90 patients were randomly selected by divided into two groups (experimental and control). The researchers met all patients the day before the surgery explained the reason and use of the picture chart for the experimental group and the routine communication methods for the control group. Therefore on the day of surgery communication was established with both groups with their own particular method. Data collection was done through a questionnaire consisting of: 1) demographic specification and clinical history, 2) Spielburger's questionnaire for obvious anxiety and 3) visual analogue scale (10cm) for patient satisfaction evaluation which is undertaken the day after surgery and after extubation. The results of the χ² test and the Fisher's exact test showed significant statistical differences between the two groups concerning the level of the patient's anxiety and satisfaction. The use of the picture chart for the experimental group, decreased patient's anxiety resulting from speech disability and increased their satisfaction with their communication with nurses as compared to the control group. Considering the beneficial results from using picture charts in establishing communication with patients, nurses with the help of devices, must try to create better relationships with intubated patients in the post-surgery period and provide them with more desirable services.